Tag Archives: wifi-tracking

The most daunting part of doing shopper measurement isn’t the analytics, it’s the data collection piece. Nobody likes to put new technology in the store; it’s expensive and it’s a hassle. And most stores feel like they have plenty of crap dangling from their ceilings already.

If you’re in that camp, but would love to have real in-store shopper measurement, there are three technologies you should consider. The first, and the one I’m going to discuss today, is your existing WiFi access points.

Most modern WiFi access points can geo-locate the signals they receive. Now you may be thinking to yourself that the overwhelming majority of shoppers don’t connect to your WiFi. But that’s okay. Phones with their WiFi enabled ping out to your access points on a regular basis even when they don’t connect to your WiFi. And, yes, it’s both possible and acceptable to use that for anonymous measurement.

What that means, is that you can use your store’s WiFi to measure the journeys for a significant percentage of your shoppers. Access point tracking is incredibly convenient. Since it’s based off your existing customer WiFi system, you already have the necessary hardware. If your equipment is modern, it’s usually just a matter of flipping a software switch to get geo-location data in the cloud.

Providers like Meraki have been gradually improving the positional accuracy of the data and they make it super-easy to enable this and get a full data feed. And if you’re equipment is older or from a vendor that doesn’t do that? It’s not a lost cause. Every reasonably modern WiFi Access Point generates a log file that includes the basic data necessary for positional triangulation. It’s not as convenient as the cloud-based feeds that come with the best systems, but if you don’t mind doing a little bit of traditional IT file wrangling, it can work almost as well. We’ll do the heavy lifting on the positioning.

The biggest downside to traditional WiFi measurement has been the lack of useful analytics. Working from the raw feed is very challenging for an enterprise (harder than just installing new devices) and the reporting and analytics you get out of the box from WiFi vendors is…well…about what you’d expect from WiFi vendors. Let’s just say their business isn’t analytics.

That’s where our DM1 platform really makes a huge difference. DM1 is an open, shopper analytics platform. It’s built to ingest ANY detailed, geo-located data stream. It can take data from your mobile app users. It can take data from dedicated measurement video cameras. It can take data from iViu passive network sniffers. Really, any measurement system that creates timestamped shopper/device and x,y coordinates can be easily ingested.

Your existing WiFi Access Point data fits that bill.

Imagine being able to take your WiFi geolocation data and with the flip of switch and no hardware install be able to do full-store pathing:

Full in-store funnels:

Even cooler, because DM1 uses statistical methods to identify Associate devices, we’ll automatically parse that WiFi data to identify shoppers and associates. That lets you track associate presence and intraday STARs for any section of the store. No changes to store operations. No compliance issues. You can even do a path analysis on the shopper journey by salesperson or sales team:

How cool is that!

And remember what I said about other data sources? DM1 can simultaneously ingest your mobile app user data and your WiFi data and let you track each as separate segments. You get the extra detail and positional accuracy for all your mobile shoppers along with the ability to rapidly swap views and see how the broader population of smartphone users is navigating your store.

If you’re wondering if there are drawbacks to WiFi measurement, the answer is yes. We see it as a great, no-pain way to get started with shopper analytics. But there are strong reasons why, to get really good measurement, you’ll need to migrate at least some stores to dedicated measurement collection. WiFi’s positional accuracy suffers in comparison to dedicated measurement devices like iViu’s or camera-based solutions. And it also measures fewer shoppers. Even compared to other means of electronic detection, you’ll lose a significant number of phones – especially IOS devices.

If you were reading closely, you’ll remember that I said there were three technologies to consider if you want to do shopper journey measurement without adding in-store hardware. WiFi is the easiest and the most widespread of these. But there are slam-dunk solutions for mobile app measurement that I’ll cover in my next post. And if you have relatively modern security cameras, there’s even a software-based solution that can help you turn that data into grist for the DM1 mill. That’s a solution we’ve been hoping for since day 1 – and it’s finally starting to become a reality.

When I describe my new company Digital Mortar to folks, the most common reaction I get is: “Can you really do this?”

Depending on their level of experience in the field, that question has one of two meetings. If they haven’t used existing in-store customer tracking solutions, the question generally means: is the technology practical and is it actually OK to use it (i.e. does it violate privacy policies)? If they have experience with existing in-store customer tracking solutions what they mean is: “does your stuff actually work as opposed to the garbage I’ve been using?”

I’m going to tackle the first question today (is the technology practical and legal) and leave the second for next time.

Is the Technology Practical?

Yes. As my post last week made clear, the various technologies for in-store customer tracking have challenges. Data quality is a real problem. There are issues with positional accuracy, visitorization, journey tracking, and even basic reliability. This is still cutting or even bleeding-edge technology. It’s like digital analytics circa 2005 not digital analytics 2017. But the technologies work. They can be deployed at scale and for a reasonable cost. The data they provide needs careful cleaning and processing. But so does almost any data set. If chosen appropriately and implemented well, the technologies provide data that is immediately valuable and can drive true continuous improvement in stores.

How Hard is it to Deploy In-Store Tracking?

Unfortunately, the in-store customer tracking technologies that don’t take at least some physical in-store installation (Wi-Fi Access Point based measurement and piggybacking off of existing security cameras) are also the least useful. Wi-Fi measurement is practical for arenas, airports, malls and other very large spaces with good Wi-Fi opt-in rates. For stores, it just doesn’t work well enough to support serious measurement. Security cameras can give you inaccurate, zone based counts and not much else. Good in-store measurement will require you install either measurement focused cameras or passive sniffers. Of the two, sniffers are lot easier. You need a lot less of them. The placement is easier. The power and cabling requirements are lower. And they are quite a bit cheaper.

Either way, you should expect that it will take a few weeks to plan out the deployment for a new store layout. This will also involve coordination with your installation partner. Typically, the installation is done over one or two evenings. No special closing is required. With sniffers, the impact on the store environment is minimal. The devices are about the size of a deck of playing cards, can be painted to match the environment and any necessary wiring is usually hidden.

After a couple week shake down, you’ll have useable measurement and a plan you can roll out to other stores. Subsequent stores with the same or similar layout can be done as quickly as your installation partner will schedule them. And the post-install shake-down period is less.

So if you’re planning a Pilot project, here’s the timeline we use at Digital Mortar:

Month 1

Select Store Targets: We typically recommend 3 stores in a Pilot – one test and two control stores with similar layout and market.

Select Initial Store

Design Implementation for the Initial Store

Train Installation Partner

Do initial 1 store installation

Month 2

Test the initial installation and tune plan if necessary

Rollout to additional stores

Provide initial reporting

Targeted analysis to develop store testing plan

Month 3

Run initial test(s)

Analyze control vs. test

Assess findings and make optimization recommendations

Evaluate pilot program

This kind of Pilot timeline gets you live, production data early in Month 2 with initial store findings not long after. And it gets you real experience with the type of analysis, testing and continuous improvement cycle that make for effective business use.

Is it Ok to Use Location Analytics?

Yes. In-store tracking technology is already widely used. The majority of major retailers have tried it in various forms. There is an established community of interest focused on privacy and compliance in location analytics (the Future of Privacy Forum) that is supported by the major technology players (including giants like Cisco who do this routinely), major retailers, most of the vendors specific to the space, and plenty of heavy-hitters from a political standpoint. They’ve published guidelines (with input from the FTC) on how to do this. In many respects, the landscape is similar to digital. To do this right, you must have a documented and published privacy policy and you MUST adhere to your own privacy policy. If you offer an online opt-out, you must provide and honor an online opt-out. If you offer an in-store opt-out, you must provide it. To abide by the privacy standards, you must treat the visitor’s phone MAC address as PII information. You must not keep and match the visitor’s MAC address without opt-in and you should make sure that is hashed or transformed when stored.

And, of course, in the EU the tracking guidelines are significantly more restrictive.

In almost all respects, this is identical to the use of cookies in the digital world. And, as with the digital world, it’s not hard to see where the blurry lines are. Using in-store customer journey tracking to improve the store is non-controversial – the equivalent of using first-party cookies to analyze and improve a website. Using appropriately described opt-ins to track and market to identified customers is fine as long as the usage is appropriately disclosed. Selling customer information begins to touch on gray areas. And identifying and marketing to users without opt-in using any kind of device fingerprinting is very gray indeed.

Bottom line? In-store customer tracking and location analytics is ready for prime-time. The technologies work. They can be deployed reasonably and provide genuinely useful data. Deployment is non-trivial but is far from back-breaking. And the proper uses of the data are understood and widely accepted.

In my next post, I’ll take up the analytic problems that have crippled existing solutions and explain how we’ve solved them.

How does it work? Can you really do this? Is it legal? Those are the questions that I get asked the most about in-store customer journey tracking. The same kind of questions, to be honest, I used to get fifteen years ago in digital analytics. And when you have to answer questions like these, you know it’s still pretty raw out there. Collection technologies are a core part of measurement – whether it’s tags in digital analytics or PCAP files for in-store customer tracking. Technology matters. And with in-store tracking, the data collection technologies aren’t half-baked, but they aren’t well-cooked either.

Here’s what you need to know:

Collection Technologies

There are four (!) common approaches to in-store customer tracking: camera, wifi, passive network and mobile apps. Each has distinct characteristics and at least some advantages and disadvantages. Camera is pretty easy to understand. The cameras used for in-store measurement are video. Each camera has on-board processors that identify people, “blob” them, and then track them across their field of view. This yields a stream of data that is positionally very accurate and can also identify basic demographics around each visitor. The anonymized data is then passed to a central server where systems like ours can use it.

Your existing WiFi system can also be used to track customer journey data. This works whether or not people login to your access points. Phones regularly ping out looking for a network and those pings – anonymized – can be triangulated to figure out the position. Put those pings together, and you have a journey. One of the best things about WiFi tracking is that almost everybody already has the necessary hardware in place. That means there’s no new installation; and most of the top-tier providers of internet access points make it super easy to route the data directly to your cloud-based system. Often at no additional cost.

Passive network sniffers are small WiFi-like devices designed explicitly for in-store measurement. They work on principles similar to WiFi but they solve some problems that WiFi doesn’t. They track multiple bands, not just passive WiFi pings, and they can deliver better positional accuracy because they can be deployed in very large numbers quite cost-effectively.

Lastly, you can use code inside a mobile application to track the customer journey. Mobile apps can deliver a steady stream of positional data and have the unique benefit of being able to tie that data to the customer’s digital in-app experience.

So what’s not to like?

Well, each technology has some significant issues.

Cameras are expensive, installation is a challenge, each camera only covers a small zone, and camera systems do a remarkably poor job stitching together the customer journey. So as typically delivered, camera systems cost a lot and deliver limited measurement.

WiFi isn’t very accurate positionally – meaning it can’t be used effectively for much beyond door-counting in smaller and mid-size retail spaces. Worse, changes in MAC randomization in the IoS world have essentially eliminated the ability of WiFi systems to passively track customers with Apple devices. That means you either depend on users to connect to your WiFi (which does yield stable measurement) or you only measure your Android customers. Two bad solutions don’t add up to a good one.

Passive network sniffers improve on WiFi in terms of positional accuracy and their ability to fingerprint devices. But they don’t solve those problems perfectly and, of course, they don’t give you the no-installation, no hardware cost convenience that WiFi did.

Measurement using mobile apps? That’s great, just like everything with mobile apps provided you can get customers to actually download the app. Depending on customer app downloads for measurement is inherently a limiting factor.

Bottom line? There are places and times for every technology and there are ways to combine the technologies to yield better results (we do that). But this isn’t measurement nirvana. No solution is perfect and you’ll find plenty of things to hate in any direction you choose.

To get more detail on the ins-and-outs of in-store customer journey tracking technology (and it’s complicated), ping me. I’ll send you a DM whitepaper that gives you everything you need to know to choose wisely!

Overbuilt. Underused. Under-siege. Mall traffic has declined precipitously in the last decade and the need to aggressively drive traffic via better experience is a matter of plain survival. That need for traffic has led to dramatic changes in the way malls are designed and executed – making them more experiental and less anchored. But if you can’t measure the impact of an experience by segment, how you can possibly drive continuous improvement?

Malls are a hybrid case of physical location measurement: a large public space but one in which elements of store tracking are clearly present. Of course, most malls already have a basic counting infrastructure. They track the high-level flow of customers and can help individual stores evaluate their overall share as well as document the populations they deliver.

But with the way malls are changing, there are opportunities and new uses for customer journey tracking that can dramatically improve mall analytics. Not only are malls becoming more experiential, anchors are becoming more diverse and traffic patterns more complex. These days, it really isn’t good enough to understand broad traffic patterns without being able to segment and group customers meaningfully. To really optimize experience and design, you need to know more than how many customers passed through. You need to understand what customers did, in what order, and in what combinations.

Fortunately (because this is a big cost driver), Malls don’t require high positional accuracy in measurement. But they absolutely require the ability to track journeys and define segments. Zone counting just won’t cut it. It’s critical to be able to measure experience usage and tie that to actual store visits and to USE that knowledge to continuously tune experiences. It’s just as important to be able to track over-time usage of the malls. A lot of interesting store analytics happens at the visit level. Visitor is far more important for a mall evaluating experience drivers. If the key metric being optimized is repeat visits and you can’t track that, what’s the point?

Finally, malls are like stadiums in that they can expect reasonable rates of wifi access and have increasingly focused on building out CRM and digital marketing efforts to drive traffic. Adding tracking data to that equation delivers far better segmentation and relevancy (and segmentation and relevancy determine success) and makes it possible to bring straightforward remarketing techniques to bear on your customer marketing. It’s no surprise that we see so many re-marketing display ads these days. It’s the only form of display that even remotely works. Re-marketing based on store visit is a big shot in the arm to mall CRM relevancy and a great way to build partnerships and deliver added value from mall analytics. And, as an added bonus, you get dramatically better insight into whether or not those CRM efforts are actually working!

Key Questions

How do mall anchor experiences draw and how do their users interact with the rest of the mall?

How do changes in experience impact store usage and success by segment?

What shopping segments exist and how can segmentation be used to maximize CRM relevancy?

What experiences create return visits & increased over time consumption?

What experience data can be used to optimize digital communications?

For more information about in-store customer tracking and analytics, drop me a line.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.